The present subject matter relates generally to data analytics, and more particularly, but not exclusively to a method and a system for generating a contextual summary of one or more charts.
Generally, Business Intelligence (BI) is a broad category of computer software solutions that enables a company or an organization to gain insights into critical operations using reporting applications and analysis tools. Therefore, charts or any representation of data in BI platforms are complex and difficult to interpret. Interpretation of charts may be possible only by people who have gained expertise in the subject matter such as data analysts, business analysts, data scientists, etc. This leads to a situation where the managers and business executives may end up spending enormous amount of time on interpreting the data rather than devising strategies to improve the performance of team and/or organization.
In the existing approach, an enterprise uses BI and data visualization tools for dashboards with different types of charts and visualizations. The reports and dashboards may help in analysing the data that is fed into the system by summarizing the enterprise data in the form of visualizations. The major drawback in the existing approach is that multiple charts are created upon analysis, to provide analysis results. Interpretation of these multiple other charts to get insights of the critical operations of the enterprise again consumes time and effort of the end user such as managers, team leaders, and business executives. Since the interpretation is manual, there exists potential scope for mistakes in interpretation of the analyzed data.
There are no existing methods or techniques that automatically analyze the data behind charts, interpret the data and present insights in the form of natural language using statistical techniques and machine learning.